Synchronization and coherence resonance in chaotic neural networks
Wang Mao-Sheng(汪茂胜)a)b), Hou Zhong-Huai(侯中怀)a)†, and Xin Hou-Wen(辛厚文)a)
a Department of Chemical Physics, University of Science and Technology of China, Hefei 230026, China; b College of Physics and Electronic Information, Anhui Normal University, Wuhu 241000, China
Abstract Synchronization and coherence of chaotic Morris--Lecar (ML) neural networks have been investigated by numerical methods. The synchronization of the neurons can be enhanced by increasing the number of the shortcuts, even though all neurons are chaotic when uncoupled. Moreover, the coherence of the neurons exhibits a non-monotonic dependence on the density of shortcuts. There is an optimal number of shortcuts at which the neurons' motion is most ordered, i.e. the order parameter (the characteristic correlation time) that is introduced to measure the coherence of the neurons has a maximum. These phenomena imply that stochastic shortcuts can tame spatiotemporal chaos. The effects of the coupling strength have also been studied. The value of the optimal number of shortcuts goes down as the coupling strength increases.
Received: 23 February 2006
Revised: 14 April 2006
Accepted manuscript online:
Fund: Project supported by the National
Natural Science Foundation of China (Grant No 20433050), the Program
for New Century Excellent Talents in University, the Fok Ying Dong
Education Foundation and the Foundation for the Author of National
Excellent
Doctoral Dissertation of China.
Cite this article:
Wang Mao-Sheng(汪茂胜), Hou Zhong-Huai(侯中怀), and Xin Hou-Wen(辛厚文) Synchronization and coherence resonance in chaotic neural networks 2006 Chinese Physics 15 2553
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